# XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging

arXiv: https://arxiv.org/abs/2007.05492 · HTML: https://mlsanity.com/paper/2007.05492/

## Key facts

- Cited by 88 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2020.

## Built on by (most cited first, 30 of 88)

- [SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification](https://mlsanity.com/paper/2105.11043/) (2021)
- [Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions](https://mlsanity.com/paper/2111.08446/) (2021)
- [SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals](https://mlsanity.com/paper/2405.17766/) (2024)
- [Data augmentation for learning predictive models on EEG: a systematic comparison](https://mlsanity.com/paper/2206.14483/) (2022)
- [SalientSleepNet: Multimodal Salient Wave Detection Network for Sleep Staging](https://mlsanity.com/paper/2105.13864/) (2021)
- [CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model](https://mlsanity.com/paper/2506.09110/) (2025)
- [DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model with Uncertainty Estimates](https://mlsanity.com/paper/2108.10600/) (2021)
- [RobustSleepNet: Transfer learning for automated sleep staging at scale](https://mlsanity.com/paper/2101.02452/) (2021)
- [L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging](https://mlsanity.com/paper/2301.03441/) (2023)
- [Improving Multimodal Learning with Multi-Loss Gradient Modulation](https://mlsanity.com/paper/2405.07930/) (2024)
- [ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training](https://mlsanity.com/paper/2107.04470/) (2021)
- [Generalizable Sleep Staging via Multi-Level Domain Alignment](https://mlsanity.com/paper/2401.05363/) (2023)
- [Brant-X: A Unified Physiological Signal Alignment Framework](https://mlsanity.com/paper/2409.00122/) (2024)
- [FoME: A Foundation Model for EEG using Adaptive Temporal-Lateral Attention Scaling](https://mlsanity.com/paper/2409.12454/) (2024)
- [Balancing Multimodal Training Through Game-Theoretic Regularization](https://mlsanity.com/paper/2411.07335/) (2024)
- [Robust learning from corrupted EEG with dynamic spatial filtering](https://mlsanity.com/paper/2105.12916/) (2021)
- [Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers](https://mlsanity.com/paper/2208.06991/) (2022)
- [Promoting cross-modal representations to improve multimodal foundation models for physiological signals](https://mlsanity.com/paper/2410.16424/) (2024)
- [U-Sleep's resilience to AASM guidelines](https://mlsanity.com/paper/2209.11173/) (2022)
- [mulEEG: A Multi-View Representation Learning on EEG Signals](https://mlsanity.com/paper/2204.03272/) (2022)
- [Deep Efficient Continuous Manifold Learning for Time Series Modeling](https://mlsanity.com/paper/2112.03379/) (2021)
- [CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities](https://mlsanity.com/paper/2304.06485/) (2023)
- [DeeperBrain: A Neuro-Grounded EEG Foundation Model Towards Universal BCI](https://mlsanity.com/paper/2601.06134/) (2026)
- [Multi-view Audio and Music Classification](https://mlsanity.com/paper/2103.02420/) (2021)
- [TransSleep: Transitioning-aware Attention-based Deep Neural Network for Sleep Staging](https://mlsanity.com/paper/2203.12590/) (2022)
- [Evaluating the structure of cognitive tasks with transfer learning](https://mlsanity.com/paper/2308.02408/) (2023)
- [Automated Sleep Staging via Parallel Frequency-Cut Attention](https://mlsanity.com/paper/2204.03173/) (2022)
- [Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring](https://mlsanity.com/paper/2207.07753/) (2022)
- [SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning](https://mlsanity.com/paper/2209.09452/) (2022)
- [S4Sleep: Elucidating the design space of deep-learning-based sleep stage classification models](https://mlsanity.com/paper/2310.06715/) (2023)

## Abstract (arXiv)

Automating sleep staging is vital to scale up sleep assessment and diagnosis to serve millions experiencing sleep deprivation and disorders and enable longitudinal sleep monitoring in home environments. Learning from raw polysomnography signals and their derived time-frequency image representations has been prevalent. However, learning from multi-view inputs (e.g., both the raw signals and the time-frequency images) for sleep staging is difficult and not well understood. This work proposes a sequence-to-sequence sleep staging model, XSleepNet, that is capable of learning a joint representation from both raw signals and time-frequency images. Since different views may generalize or overfit at different rates, the proposed network is trained such that the learning pace on each view is adapted based on their generalization/overfitting behavior. In simple terms, the learning on a particular view is speeded up when it is generalizing well and slowed down when it is overfitting. View-specific generalization/overfitting measures are computed on-the-fly during the training course and used to derive weights to blend the gradients from different views. As a result, the network is able to retain the representation power of different views in the joint features which represent the underlying distribution better than those learned by each individual view alone. Furthermore, the XSleepNet architecture is principally designed to gain robustness to the amount of training data and to increase the complementarity between the input views. Experimental results on five databases of different sizes show that XSleepNet consistently outperforms the single-view baselines and the multi-view baseline with a simple fusion strategy. Finally, XSleepNet also outperforms prior sleep staging methods and improves previous state-of-the-art results on the experimental databases.